Metrology targets are designed to enable the measurement of parameters that indicate the quality of wafer production steps and quantify the correspondence between design and implementation of structures on the wafer. Imaging metrology targets as specific structures optimize the requirements for device similarity and for optical image measurability and their images provide measurement data.
Typical target designs utilize inversion symmetry of the image printed at each layer. The images used for the overlay target are a box-in-box type of target, consisting of two or more concentric rectangles, each printed at a relevant process layer. Deviation from common center of symmetry of each of the rectangles is reported as overlay. Another method for the same purpose uses AIM (Advanced Imaging Metrology) targets, in which images used for overlay measurement are represented as a pair of gratings for each axis. The center of symmetry of each layer is determined by correlation of two gratings at each layer, separately for X and Y axes. Such targets allow for frequency filtering, reducing the effect of random noise on overlay calculation. Although AIM target possess multiple convenient features, as a periodic pattern that allows for convenient sub-pixel interpolation, and mathematical noise reduction, they often suffer from significant suppression of high harmonics by the optical system, leaving the image of virtually single frequency. In such a case displacement (overlay) and asymmetry of the target are indistinguishable and accurate overlay measurement cannot be verified.                Hence, in current AIM targets the frequencies observed in the image are dictated solely by the pitch (period) of the structure, as the relative phase and intensity of the harmonics are determined by the shape of the repetitive structure (generating feature). In particular, symmetric features, based on their extent and particular details of the light scattering, often exclude generation of particular diffraction orders, making image analysis algorithms rather vulnerable to errors.        